Search Results for author:
Found 11 papers, 2 papers with code
SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering
In particular, local editing methods, which directly update model parameters, are more suitable for updating a small amount of knowledge.
Span-based joint entity and relation extraction augmented with sequence tagging mechanism
On the one hand, the core architecture enables our model to learn token-level label information via the sequence tagging mechanism and then uses the information in the span-based joint extraction; on the other hand, it establishes a bi-directional information interaction between NER and RE.
Joint Entity and Relation Extraction
named-entity-recognition
+3
A Two-Phase Paradigm for Joint Entity-Relation Extraction
An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search
Textual adversarial attacks expose the vulnerabilities of text classifiers and can be used to improve their robustness.
Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes
Few-shot named entity recognition (NER) enables us to build a NER system for a new domain using very few labeled examples.
Topic-Grained Text Representation-based Model for Document Retrieval
Document retrieval enables users to find their required documents accurately and quickly.
Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition
Previous research has demonstrated that the two paradigms have clear complementary advantages, but few models have attempted to leverage these advantages in a single NER model as far as we know.
Deep Domain Adaptation for Pavement Crack Detection
In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available.
Boosting Span-based Joint Entity and Relation Extraction via Squence Tagging Mechanism
To solve this problem, we pro-pose Sequence Tagging enhanced Span-based Network (STSN), a span-based joint extrac-tion network that is enhanced by token BIO label information derived from sequence tag-ging based NER.
Joint Entity and Relation Extraction
named-entity-recognition
+4
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations
Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction.
Convolutional Neural Networks to Enhance Coded Speech
Enhancing coded speech suffering from far-end acoustic background noise, quantization noise, and potentially transmission errors, is a challenging task.
